In this article, we propose an interactive agent model in an open and heterogeneous multi-agent system (MAS).
Our model allows agents to autonomously communicate between each other through semantic heterogeneity.
The communication problem can be expressed by the calculation based on the abilities acquired in the receiver
agent, compared to the message sent by the sender agent. Hence, the semantic heterogeneity should be
resolved in the message processing. The agent can autonomously enrich its own ontology by using semantic
negotiation approach in several steps. We develop firstly, a model using an ontology alignment framework.
Then, we enhance a similarity measure to select the most similar pairs by combining a psychological knowledge
of the relevance, the resemblance, and the non-symmetry of similarity. At the end, we suggest a protocol
for supporting semantic negotiation. In order to explain our approach, we implement a simple benchmark production
system on JADE.

One of the major challenges of computer system design is the management and conservation of energy while
satisfying QoS requirements. Recently, Dynamic Voltage and Frequency Scaling (DVFS) has been integrated
to various embedded processors as a mean to increase the battery life without affecting the responsiveness
of tasks. This paper proposes an enhancement for I-codesign methodology [1] optimizing the energy consumption
of the designed system.We propose an energy aware real-time scheduling algorithm. This algorithm
makes use of the defferable server for the scheduling of aperiodic tasks along with DVFS. Simulation results
demonstrate a decrease in the resulting energy consumption compared to the previously published work.

In this work, the problem of designing sensor-based controllers allowing to navigate in orchards is considered. The navigation techniques classically used in the literature rely on path following using metric maps and metric localization obtained from onboard sensors. However, it appears promising to use sensor-based approaches together with topological maps for two main reasons: first, the environment nature is rather changing and second, only high-level information are sufficient to describe it. One of the key maneuver when navigating through an orchard is the u-turn which must be performed at the end of each row to reach the next one. This maneuver is generally performed using only dead reckoning because of the lack of dedicated sensory data. In this paper, we propose two sensor-based control laws allowing to perform u-turns, improving the performance quality. They allow following particular spirals which are defined from laser rangefinder data and adapted to realize the desired maneuver. Their stability is studied and their performances are thoroughly examined. Finally, they are embedded in a complete navigation strategy to show their efficiency in our agricultural context.

Service robots are nowadays more and more common on diverse environments. In order to provide useful
services, robots must not only identify different objects but also understand their use and be able to extract
characteristics that make useful an object. In this work, a framework is presented for recognize home furniture
by analyzing geometrical features over point clouds. A fast and efficient method for horizontal and vertical
planes detection is presented, based on the histograms of 3D points acquired from a Kinect like sensor onboard
the robot. Horizontal planes are recovered according to height distribution on 2D histograms, while vertical
planes with a similar approach over a projection on the floor (3D histograms). Characteristics of points belonging
to a given plane are extracted in order to match with planes from furniture pieces in a database. Proposed
approach has been proved and validated in home like environments with a mobile robotic platform.

The detection of new Android malware is far from being a relaxing job. Indeed, each day new Android
malware appear in the market and it remains difficult to quickly identify them. Unfortunately users still pay
the lack of real efficient tools able to detect zero day malware that have no known signature. The difficulty is
that most of the existing approaches rely on static analysis coupled with the ability of malware to hide their
malicious code. Thus, we believe that it should be easier to study what malware do instead of what they
contain. In this article, we propose to unmask Android malware hidden among benign applications using the
observed information flows at the OS level. For achieving such a goal, we introduce a simple characterization
of all the accountable information flows of a standard benign application. With such a model for benign
apps, we lead some experiments evidencing that malware present some deviations from the expected normal
behavior. Experiments show that our model recognizes most of the 3206 tested benign applications and spots
most of the tested sophisticated malware (ransomware, rootkits, bootkit).

The task allocation problem in a distributed environment is one of the most challenging problems in a multiagent
system. We propose a new task allocation process using deep reinforcement learning that allows cooperating
agents to act automatically and learn how to communicate with other neighboring agents to allocate tasks
and share resources. Through learning capabilities, agents will be able to reason conveniently, generate an appropriate
policy and make a good decision. Our experiments show that it is possible to allocate tasks using
deep Q-learning and more importantly show the performance of our distributed task allocation approach.

Adaptive systems are able to modify their behaviors to cope with unpredictable significant changes at run-time such as component failures. These systems are critical for future project and other intelligent systems. Reconfiguration is often a major undertaking for systems: it might make its functions unavailable for some time and make potential harm to human life or large financial investments. Thus, updating a system with a new configuration requires the assurance
that the new configuration will fully satisfy the expected requirements. Formal verification has been widely used to guarantee that a system specification satisfies a set of properties. However, applying verification techniques at run time for any potential change can be very expensive and sometimes unfeasible. In this paper, we propose a new verification approach to deal with the formal verification of these reconfiguration scenarios. New reconfigurable CTL semantics is introduced to cover the verification of reconfigurable properties. It consists of two verification steps: design time and run-time verification. A railway case study will be also presented.

The designs of reconfigurable embedded real-time energy harvesting multiprocessor systems are evolving for higher energy efficiency, high-performance and flexible computing. Energy management has long been a limiting factor in real-time embedded systems. A reconfiguration is defined as a dynamic operation that offers to the system the capability to adjust and adapt its behavior i.e., scheduling policy, power consumption, or to modify the applicative functions i.e., add-remove-update software tasks, according to environment and the fluctuating behavior of renewable source. This paper provides an implementation of reconfigurable multiprocessor energy harvesting systems. The objective of this work is to develop software components for the design of real-time operating systems. We propose a novel adaptive approach in order to address the limitations in energy harvesting systems. We develop a reconfigurable real-time energy harvesting system based on POSIX implementation. The proposed approach is assessed from two aspects, energy management and real-time scheduling. Experimental results show the effectiveness of the proposed approach compared with state-of-the-art techniques.

Model-based control has become more and more popular in the legged robots community in the last ten
years. The key idea is to exploit a model of the system to compute precise motor commands that result
in the desired motion. This allows to improve the quality of the motion tracking, while using lower gains,
leading so to higher compliance. However, the main flaw of this approach is typically its lack of robustness to
modeling errors. In this paper we focus on the robustness of inverse-dynamics control to errors in the inertial
parameters of the robot. We assume these parameters to be known, but only with a certain accuracy. We
then propose a computationally-efficient optimization-based controller that ensures the balance of the robot
despite these uncertainties. We used the proposed controller in simulation to perform different reaching tasks
with the HRP-2 humanoid robot, in the presence of various modeling errors. Comparisons against a standard
inverse-dynamics controller through hundreds of simulations show the superiority of the proposed controller
in ensuring the robot balance.

The features of probabilistic adaptive systems are especially the uncertainty and reconfigurability. The structure of a part of the system may be totally unknown or partially unknown at a particular time. Openness is also an inherent property, as agents may join or leave the system throughout its lifetime. This poses severe challenges for state-based specification. The languages in which probabilistic reconfigurable systems are specified should be clear and intuitive, and thus accessible to generation, inspection and modification by humans. This paper introduces a new approach for specifying adaptive probabilistic discrete event systems. We introduce the semantics of GR-TNCES to optimize the specification of unpredictable timed reconfiguration scenario running under resources constraints. We also apply this approach to specify the requirements of an automotive transport system and we evaluate its benefits.